磁导率
岩石物理学
石油工程
水力压裂
储层模拟
煤层气
煤
工作流程
相对渗透率
各向异性
储层建模
地质学
煤矿开采
岩土工程
工程类
计算机科学
多孔性
化学
废物管理
生物化学
物理
量子力学
数据库
膜
作者
Honja Miharisoa Ramanandraibe,Ayrton Ribeiro,Raymond L. Johnson,Zhenjiang You
出处
期刊:The APPEA journal
[CSIRO Publishing]
日期:2021-01-01
卷期号:61 (1): 106-106
被引量:4
摘要
Many coal seam gas (CSG) reservoirs (also known as coalbed methane) can have low permeability, require stimulation to produce economic rates and often exhibit pressure-dependent permeability (PDP) behaviour. Defining PDP behaviour in coal using reservoir simulation is a non-unique problem based on the uncertainty in coal properties and input parameters. Recent research demonstrated that an integrated analysis coupling of a diagnostic fracture injection test analysis, hydraulic fracture modelling and reservoir simulation can better characterise PDP behaviour in order to evaluate stimulation effectiveness in coals (Johnson et al. 2020). The present work aims to improve the recently developed model by including multilayer and permeability anisotropy effects. A reservoir model with multiple coal layers is established in a pressure-dependent reservoir simulator, based on the image log interpretations. Permeability anisotropy in the formation is realised by introducing heterogeneous distribution of permeability in different directions. Modelling results indicate effects of aspect ratio between multilayers on the pressure distribution and production history. A lower permeability anisotropy ratio yields better well productivity, and higher stimulation is required to increase the stimulated reservoir volume to maximise gas recovery. The improved model and workflow are applicable to other CSG fields for defining key variables where hydraulic fracturing performance has been unable to overcome limitations based on pressure dependency, often accompanied by low-permeability behaviour. This workflow has applications in Australia and many areas (e.g. China and India) exhibiting low-permeability and PDP behaviour and where only typically collected field data is available.
科研通智能强力驱动
Strongly Powered by AbleSci AI